In order to gain greater insight from data analysis, more operations, businesses and services are collecting business metrics and other data to provide a clearer picture of what is happening day-to-day, month-to-month, year-over-year or across any other period of time. Revenue analysis, marketing studies, and resource forecasting are some of the many different types of applications for which collected data may be useful. However, as the amount of data collected grows, it becomes increasingly difficult to validate whether the reported content is correct. Errors may infiltrate large data sets and escape detection as manual validation becomes ineffective for ensuring that collected data is correct, and therefore useful. Moreover, as the sources of collected data may grow in number and diversity, it may be difficult even for automated validation techniques to cope with the complex validation scenarios that occur validating large and diverse data sets require.
While embodiments are described herein by way of example for several embodiments and illustrative drawings, those skilled in the art will recognize that the embodiments are not limited to the embodiments or drawings described. It should be understood, that the drawings and detailed description thereto are not intended to limit embodiments to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope as defined by the appended claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including, but not limited to.